Passive wireless surveillance method based on Shannon entropy of the signal strength space

Efficient wireless networks have significantly motivated the use of radio signals for intrusion detection and surveillance. While moving through an area covered with 2.4GHz radio network, a human body interferes with radio signals, resulting in strong variations of the signal strength. In this paper, we propose the method which recognizes human presence by analyzing principal components from the matrix of samples that present signal strength variations. Mainly based on smart power outlets and communication signals, the original environment is not visually modified, but a certain level of sensorial intelligence is introduced without additional sensors and installation costs. Principal component analysis combined with Shannon entropy enhances the detection accuracy and improves the overall robustness of the method.